I have had the following informative and constructive interaction with Zong-Liang Yang of the University of Texas in Austin and Zhongfeng Xu of the Institute of Atmospheric Physics of the Chinese Academy of Sciences. My summary comment is at the send of the post.

Comment By Liang and Zhongfeng

“A more accurate regional projection of future climate is always useful for making decisions even the policymakers do not require them. In this paper we are trying to improve regional projections of future climate. This does not mean IDD can produce a 100% accurate projection. As a matter of fact, it is impossible to make a 100% accurate projection. However we still can make climate projections better. Our result shows IDD can better simulate the future climate mean states and extreme events than TDD.”

My Response:

I disagree. What you have shown are systematic errors in regional model simulations using a set of hindcast runs, which can be used to correct (adjust) simulation results for another subset. This is analogous to the Method of Model Output Statistics [MOS] and is certainly an appropriate approach. However, in a future run (after 2012), you are running the regional climate model with lateral boundary conditions and (interior nudging if used) which will have a different climatology of input. Those lateral boundary conditions will have systematic biases too, but likely will be different (but as you say there is no way to correct for from
observations).

Comment By Liang and Zhongfeng

Yes, there is no way to correct GCM biases in simulating climate change but we still can correct GCM systemic biases in simulating climatological means. A detailed discussion is as follows.

In the paper, we are trying to correct the systematic biases (which by no means represent all errors but only the biases in climatological mean and variance) in the GCM to prevent these systematic biases from being passed into the RCM through the LBC.

There are different biases between GCM simulations and the NCEP reanalysis, which can be reflected in the phase of interannual variations (e.g. the GCM simulates a positive anomaly but the NCEP shows a negative anomaly in a individual year), climate change (e.g. the GCM simulates a 0.1C/decade warming trend but the NCEP shows a 0.2C/decade warming trend), the climatological mean and the variance, and so on and so forth. Our bias correction method only corrects the climatological mean bias and variance bias because these are the GCM systemic biases and they usually do not change too much with time. Please refer to Fig. 1 in the paper. The differences in mean and variance between NNRP and the original GCM simulation do not change too much during the period of 1980-2010. We believed the conclusion remains the same if we plot the figure from 1950-2010. This means the GCM climatological mean bias and variance bias are generally time-independent. These biases could result from some parameterization schemes or something else. If a parameterization scheme tends to produce a negative bias in the current climate, then we have reason to believe it will produce a negative bias in the future climate. It is this type of biases we can correct. With regard to the biases such as the phase of interannual variation and climate change from the past to the future, we do not correct them because we do not know what would happen in the future. But we have the confidence to correct the climatological mean bias and the variance bias in the GCM. The GCM climatological mean bias over the future period is composed of the GCM systemic bias over the past period and the climate change bias (from the past to the future). While we only correct the former one in our current paper, our results show that the GCM climatological mean and variance bias corrections lead to a better downscaled mean climate and extreme event statistics relative to the traditional dynamical downscaling approach (TDD).

We would like to explain the GCM bias correction further through the following figure.

The black solid line represents the NNRP data – The blue solid line represents the original GCM simulation -The red solid line represents the bias corrected GCM simulation- The dotted lines represent the climatological means over the past (green shaded area) and the future periods, respectively.

The bias correction method proposed in the paper involves shifting (i.e., removing the climatological mean bias) and scaling (i.e., removing the variance bias) the original GCM simulation at each model grid. As we can see, the bias-corrected GCM has the same climatological mean as NNRP over the past period (red and black dotted line on the left). However, their climatological means may be different over the future period (red and black dotted line on the right). This difference results from the GCM biases in the climate change simulation (i.e. the difference between the past and the future mean climate), which can not be removed by our GCM bias correction method. Even so, for the future period, the difference between the red dotted line and the black dotted line is still smaller than the difference between the blue dotted line and the black dotted line because the GCM systemic bias in climatological mean has been removed. This means the bias corrected GCM is closer to NNRP over the “future” period than the original GCM simulation does. The improvement is mainly due to the GCM climatological mean bias correction. Note that the bias correction to the future GCM simulation does NOT need the NNRP data over the “future” period.

My Comment:

An even more serious issue is that simulating hindcast regional climate statistics is just one requirement. The regional climate model to have value beyond reanalyses [as well as for straightforward interpolation of the global model projection results onto a finer terrain and landscape map], must skillfully predict CHANGES in the regional climate statistics. This was not done in your paper.

Comment By Liang and Zhongfeng

We did not analyze the CHANGES simulated by the regional models because the GCM bias correction does not correct the CHANGES bias simulated by GCM. So it seems no reason we expect IDD can produce better CHANGES projection than TDD.

“In our study, we have 63-year NNRP data (1948-2010) and we also run CAM over the same period. Then we divided the 63-year into two periods: the ‘past’ (1948-1979) and the ‘future’ (1980-2010). We correct CAM biases of the future simulation based on the CAM past simulation and NNRP past data. In other words, we do NOT need future observations when correcting CAM future biases. The bias corrected CAM future simulation was used to drive WRF (i.e. the IDD experiment). The IDD experiment was compared with the WRF run driven by NNRP future data to assess the performance of IDD in simulating the future climate. In addition, the CAM bias corrections only correct the CAM climatological mean bias and the variance bias. The bias correction method retains the CAM simulated climate change from the past to the future plus the phase of interannual variation. We assume that the CAM systemic biases do not change over time when correcting CAM biases.

For climate projections, the future climate change may be more important than the future climatological means. Unfortunately, the CAM bias correction method can not correct CAM biases in simulating future climate change. Even so the IDD is still better than the TDD in regional projection of future climate.”

what is the value of the approach in this context? That is the crux as to why they are needed by the impacts communities. How can it be presented as skillful?

Comment By Liang and Zhongfeng

In terms of the value of the IDD we think there are at least two applications in which we can expect better downscaled climate: (1) regional projections of future climate; (2) sensitivity studies.

(1) Regional projections of future climate: As the paper showed, IDD is able to produce better climatological means and extreme event statistics relative to TDD although the GCM bias correction method can not remove all biases in GCM. Therefore IDD is able to provide more useful information than TDD in the impacts studies. Of course people care about the climate change more than the climatological means. However the climate change prediction is a very challenging and complicated issue. If someone can significantly improve the climate change prediction that would be a great contribution to the science community. We did not find a good way to improve the future climate change projection yet.

(2) Sensitivity studies: IDD can also be applied to downscale GCM sensitivity simulations. In this case the GCM bias correction should be considered in a different way. Namely the GCM control run is corresponding to the “past GCM simulation” in the paper and the GCM sensitivity run is corresponding to the “future GCM simulation” in the paper. The difference between the GCM control run and the sensitivity run is corresponding the “climate change (from the past to the future) simulated by GCM” in the paper. In this way the difference between various GCM simulations (control run and sensitivity run) can be retained and passed into RCM, and further impact the downscaled simulations.

My Comment

As I wrote in response to the first comment, the “future. (1980-2010)” must be able to predict that part of the results which involve CHANGES in the regional climate statistics. What fraction of your results “past” (1948-1979) and the “future” (1980-2010) involve changes in the regional statistics, and how well are they replicated based on the reanalyses?

Comment By Liang and Zhongfeng

We assess the IDD performance by comparing GCM-driven WRF simulation with reanalysis drive-simulation rather than comparing with NCEP reanalysis because they are in different resolutions. WRF simulation: 60km; NCEP2 reanalysis: 2.5 degree. We did not compare GCM-driven WRF simulation with NARR, either, because we want to isolate LBC influences. The difference between GCM-driven WRF simulation and NARR results from both the GCM and RCM biases. Two biases could appear in same sign or opposite sign. The opposite sign biases would offset each other then produces a correct simulation for the wrong reasons.

Except the increasing CO2 concentration in RCM, the climate change in RCM mainly comes from GCM in our simulation. So the climate changes in RCM strongly depends GCM simulation. If GCM is able to produce a good climate change simulation, the RCM is supposed to do a better job, too, vice versa.

We did not analyze the climate CHANGES in the regional statistics. We guess that the performance of IDD in simulating climate CHANGES is similar with TDD.

My Comment

On your comment

“In our paper, we corrected CAM biases of atmospheric variables such as air temperature and geopotential height. The bias correction method can also be applied to Type 4 dynamical downscaling only with SST bias correction being included as well.”

It would be valuable for you to expand on this approach, as I really feel your methodology has its real power for type 3 downscaling (i.e. seasonal prediction).

My Comment:

Since the global model is not adjusted outside the domain of the regional climate model, the systematic biases are continually being fed into the regional model in the future scenarios. How do you feel you handle this continual insertion of what are actually errors?”

Comment By Liang and Zhongfeng

We corrected GCM biases at each global model grid (including the areas both inside and outside the domain of regional climate model as well as the boundary regions of RCM). However, only the GCM data over the RCM boundary was used in the dynamical downscaling run since the GCM data was used as the lateral boundary condition(LBC) of RCM. The GCM data outside and inside the RCM domain do not impact RCM simulation. In future study, we will further employ spectral nudging in WRF. By doing this the bias corrected GCM data inside the RCM domain will be fed into RCM as well. Hopefully the spectral nudging will reduce the RCM system biases in future climate dynamical downscaling. The combination of GCM bias correction and spectral nudging are expected to reduce both the GCM bias and RCM bias and in turn produce a downscaled simulation closer to observation. The numerical simulations with GCM bias correction and nudging had been finished and we are going to work on them shortly. Hope receive your comments as well in future.

Liang: please correct me if any response are wrong or you have different opinions.

My Final Comment

This is a very informative discussion by two outstanding climate scientists. Their method of adjusting for systematic biases in the global models is an important scientific contribution.

It first shows the level of error in the global models even for the current climate.

It also provides a method to improve on long-term model predictions, particularly on the seasonal time scale.

However, in terms of multi-decadal climate projections (predictions), their results show that they are not adding value. They wrote that they do “not analyze the climate CHANGES in the regional statistics. We guess that the performance of IDD in simulating climate CHANGES is similar with TDD.” While they list above under #1 that they are providing “regional projections of future climate”, if the model’s cannot be shown to accurately predict CHANGES in climate statistics, they are not providing skillful projections to the impacts community.

Indeed, the impacts community should just go directly to the reanalyses for their climate statistics. They could insert arbitrary perturbations in that weather data (e.g. add 1C to summer temperatures, reduce summer rainfall by 10%, ect) in order to assess risk to the resource of interest to them. Using model predictions for decades from now, which have no demonstrated skill at predicting changes in regional climate statistics, is misleading policymakers.